AWS Certified Machine Learning Specialty (MLS-C01)

The AWS Certified Machine Learning Specialty (MLS-C01) were last updated on today.

Viewing page 4 out of 57 pages.
Viewing questions 16-20 out of 285 questions

Disclaimers:

- ExamTopics website is not related to, affiliated with, endorsed or authorized by Amazon.and Azure
- Trademarks, certification & product names are used for reference only and belong to Amazon.and Azure

Topic 1 - Exam A

Question #16 Topic 1

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression. During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable. What should be done to reduce the impact of having such a large number of features?

A Perform one-hot encoding on highly correlated features.
B Use matrix multiplication on highly correlated features.
C Create a new feature space using principal component analysis (PCA)
D Apply the Pearson correlation coefficient.

Suggested Answer: C
NOTE:

Question #17 Topic 1

A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only. What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

A Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
B Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
C Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
D Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

Suggested Answer: D
NOTE:

Question #18 Topic 1

A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours. With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s). Which visualization will accomplish this?

A A histogram showing whether the most important input feature is Gaussian.
B A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
C A scatter plot showing the performance of the objective metric over each training iteration.
D A scatter plot showing the correlation between maximum tree depth and the objective metric.

Suggested Answer: B
NOTE:

Question #19 Topic 1

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users. What should the Specialist do to meet this objective?

A Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR
B Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
C Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR
D Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR

Suggested Answer: B
NOTE: Many developers want to implement the famous Amazon model that was used to power the ג€People who bought this also bought these itemsג€ feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed. Reference: https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/

Question #20 Topic 1

A large consumer goods manufacturer has the following products on sale: * 34 different toothpaste variants * 48 different toothbrush variants * 43 different mouthwash variants The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched. Which solution should a Machine Learning Specialist apply?

A Train a custom ARIMA model to forecast demand for the new product.
B Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
C Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
D Train a custom XGBoost model to forecast demand for the new product.

Suggested Answer: B
NOTE: The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future. Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html